CATALYST: 6-Week AI Engineering Intensive
Transform your developers into production-ready AI systems builders through hands-on, business-driven learning.
What Is CATALYST?
CATALYST is an intensive, cohort-based training program designed exclusively for corporate engineering teams. Unlike traditional training that focuses on theory, CATALYST is 100% outcome-driven: your engineers learn by building and deploying production AI systems.
Key Details
Duration
6 weeks total - Weeks 1-3: Remote, part-time | Weeks 4-6: Onsite, full-time
Time Commitment
Remote phase: 8-10 hours/week | Onsite phase: Full-time (40-50 hours/week)
Cohort Size
6-10 participants (small groups for personalized attention)
Locations
Toronto, Vancouver, San Francisco
What's Included
- ✓ Expert instruction from practicing AI engineers
- ✓ Live sessions + self-paced labs
- ✓ 3 capstone projects aligned to your business
- ✓ Housing & meals (onsite phase)
- ✓ Cloud compute credits ($500/participant)
- ✓ LLM API access (OpenAI, Anthropic)
- ✓ Observability tools (LangSmith, LangFuse)
- ✓ Verified certification upon completion
- ✓ 30-day post-program support
- ✓ Alumni community access
What Your Team Will Learn
6 weeks. 3 phases. Production-ready skills.
Week 1: AI Fluency & RAG Systems
Remote, Part-Time
Learning Objectives:
- • Understand modern LLM capabilities
- • Master prompt engineering
- • Build RAG applications
- • Work with vector databases
Technical Topics:
- • LLM fundamentals (GPT-4, Claude)
- • API integration
- • Vector databases (Pinecone, Weaviate)
- • Embedding models & semantic search
Week 2: AI Agents & Multi-Agent Systems
Remote, Part-Time
Learning Objectives:
- • Design autonomous AI agents
- • Orchestrate multi-agent systems
- • Build tool-using agents
- • Understand agent frameworks
Technical Topics:
- • Agent architectures (ReAct)
- • LangGraph for workflows
- • Tool use & function calling
- • Multi-agent coordination
Week 3: Fine-Tuning & Advanced Techniques
Remote, Part-Time
Learning Objectives:
- • When to fine-tune vs. prompt engineer
- • Execute supervised fine-tuning
- • Optimize context and token usage
- • Prepare for production deployment
Technical Topics:
- • Fine-tuning strategies
- • Data preparation & formatting
- • Model evaluation & testing
- • Context window optimization
Weeks 4-6: Production Intensive
Onsite, Full-Time
Full-time, in-person intensive at our Toronto, Vancouver, or San Francisco campus. Housing and meals provided.
Focus:
- • Deploy 3 capstone projects to production
- • Infrastructure & observability
- • Scaling, optimization, security
- • Daily mentorship & code reviews
Deliverables:
- • 3 production AI systems
- • Architecture diagrams
- • Documentation & runbooks
- • Final presentation to leadership
Build AI Systems That Matter
Your team doesn't build toy projects or tutorials. They solve real business problems with AI—and deploy the solutions to production.
Customer Support AI Agent
Reduce ticket volume with an intelligent agent that handles common questions and escalates complex issues.
Tech: RAG, LangGraph, vector DB, Slack integration
Document Intelligence System
Extract insights from unstructured documents with AI-powered analysis and Q&A.
Tech: Document parsers, embeddings, RAG, fine-tuning
Sales Enablement Chatbot
Help sales teams find relevant case studies, pricing info, and competitive intelligence instantly.
Tech: RAG, multi-agent system, CRM integration
Your IP, Your Value
All capstone projects are owned entirely by your company (full IP rights), deployed to your production infrastructure, and documented for long-term maintenance.
Ideal Participant Profile
You're a Great Fit If...
3+ years software engineering experience
You're comfortable building production systems and understand software development lifecycle.
Proficient in Python
You don't need to be an expert, but you should be productive in the language.
Understand APIs and web services
You've integrated third-party APIs or built REST/GraphQL endpoints.
Eager to learn AI
You're curious about AI, ready to dive deep, and excited to become an internal expert.
Prior AI Experience? Not Required.
No PhD needed
We've trained engineers with zero AI background. We start from fundamentals.
No data science degree required
This isn't a data science program. It's AI engineering—focused on building systems.
No ML theory prerequisites
We teach practical application, not academic theory. You'll learn what you need to ship code.
Transform Your Team. Start Here.
Schedule a discovery call to discuss your team's needs, explore capstone project ideas, and determine if CATALYST is the right fit.